System and method for probability based determination of estimated oxygen saturation

ABSTRACT

Present embodiments include providing an initial estimate of a value representative of a blood flow characteristic at a current timestep, and determining a probability distribution of transition, wherein the probability distribution of transition includes potential values of the blood flow characteristic at the current timestep with associated probabilities of occurrence based solely on the initial estimate. Present embodiments further include obtaining an initial measurement of the blood flow characteristic, and determining a probability distribution of measured values, wherein the probability distribution of measured values includes potential values of the blood flow characteristic at the current timestep with associated probabilities of occurrence based on the initial measurement. Further, present embodiments include combining the probability of distribution of transition with the probability of distribution of measured values to determine a meaningful blood flow characteristic value, and posting the meaningful blood flow characteristic value.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of patent application Ser. No.11/524,167, entitled “System and Method for Probability BasedDetermination of Estimated Oxygen Saturation”, filed Sep. 20, 2006,which is herein incorporated by reference in its entirety.

BACKGROUND

1. Field of the Invention

The present invention relates generally to medical devices. Moreparticularly, the present invention relates to estimating blood oxygensaturation in a patient.

2. Description of the Related Art

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the present invention,which are described and/or claimed below. This discussion is believed tobe helpful in providing the reader with background information tofacilitate a better understanding of the various aspects of the presentinvention. Accordingly, it should be understood that these statementsare to be read in this light, and not as admissions of prior art.

Pulse oximetry may be defined as a non-invasive technique thatfacilitates monitoring of a patient's blood flow characteristics. Forexample, pulse oximetry may be used to measure blood oxygen saturationof hemoglobin in a patient's arterial blood and/or the patient's heartrate. Specifically, these blood flow characteristic measurements may beacquired using a non-invasive sensor that passes light through a portionof a patient's blood perfused tissue and photo-electrically senses theabsorption and scattering of light through the blood perfused tissue. Atypical signal resulting from the sensed light may be referred to as aplethysmographic waveform. Once acquired, this measurement of theabsorbed and scattered light may be used with various algorithms toestimate an amount of blood constituent in the tissue. It should benoted that the amount of arterial blood in the tissue is time varyingduring a cardiac cycle, which is reflected in the plethysmographicwaveform.

The accuracy of blood flow characteristic estimations obtained via pulseoximetry depends on a number of factors. For example, variations inlight absorption characteristics can affect accuracy depending on where(e.g., finger, foot, or ear) the sensor is applied on a patient ordepending on the physiology of the patient. Additionally, various typesof noise and interference can create inaccuracies. For example,electrical noise, physiological noise, and other interference cancontribute to inaccurate blood flow characteristic estimates. Somesources of noise are consistent, predictable, and/or minimal, while somesources of noise are erratic and cause major interruptions in theaccuracy of blood flow characteristic measurements. Accordingly, it isdesirable to provide a system and method that continues to providesubstantially accurate blood flow characteristic measurements duringinterference and noisy periods as well as during periods with little orno noise.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantages of the invention may become apparent upon reading thefollowing detailed description and upon reference to the drawings inwhich:

FIG. 1 shows a perspective view of a basic embodiment of a pulseoximeter system in accordance with an exemplary embodiment of thepresent invention;

FIG. 2 is a detailed block diagram of the pulse oximeter system of FIG.1;

FIG. 3 is a graph of an exemplary plethysmographic waveform which may begenerated by the system of FIG. 1;

FIG. 4 is a graph of an exemplary plethysmographic waveform, wherein thewaveform includes interference due to noise;

FIG. 5 is a diagram representing predictive paths and potential SpO₂values based on a previous SpO₂ reading in accordance with an exemplaryembodiment of the present invention;

FIG. 6 is a diagram representing predictive paths and the potential SpO₂values for the related timestep based on an initial SpO₂ reading for thecurrent timestep in accordance with an exemplary embodiment of thepresent invention; and

FIG. 7 is a flow diagram of an algorithm employing probability densityfunctions in accordance with embodiments of the present disclosure;

FIG. 8 is a flow diagram of an algorithm for determining an appropriatevalue to post for a patient's SpO₂ level in accordance with an exemplaryembodiment of the present disclosure; and

FIGS. 9, 10, and 11 are representations of data acquired by implementingand testing present embodiments against synthetic data using Viterbi'salgorithm in accordance with an exemplary embodiment of the presentinvention.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

One or more specific embodiments of the present invention will bedescribed below. In an effort to provide a concise description of theseembodiments, not all features of an actual implementation are describedin the specification. It should be appreciated that in the developmentof any such actual implementation, as in any engineering or designproject, numerous implementation-specific decisions must be made toachieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which may vary from oneimplementation to another. Moreover, it should be appreciated that sucha development effort might be complex and time consuming, but wouldnevertheless be a routine undertaking of design, fabrication, andmanufacture for those of ordinary skill having the benefit of thisdisclosure.

Embodiments of the present invention relate to providing an estimationof blood oxygen saturation (SpO₂) at a particular time or over adesignated time period based on a previous SpO₂ estimation, relatedprobability estimations, initial SpO₂ measurements, and a noise valueassociated with the readings. Rather than storing or utilizing numeroushistorical data points to facilitate reducing the effects of noise, inaccordance with present embodiments it may be assumed that the bloodoxygen saturation value can be predicted based on an immediately priorvalue of the blood oxygen saturation. Specifically, in accordance withpresent embodiments, first and second portions of a determination may becombined to establish a probable actual SpO₂ value based on a prior SpO₂value and an initial SpO₂ measurement. In other words, presentembodiments estimate what the actual SpO₂ value should be based on apreceding SpO₂ value and the value of an initial SpO₂ measurement. Oncethe probable actual SpO₂ value is determined, that value may be postedas the calculated value.

FIG. 1 shows a perspective view of an exemplary embodiment of a pulseoximeter system 10. The system 10 includes a pulse oximeter or monitor12 that communicatively couples to a sensor 14. The sensor 14 mayinclude a sensor cable 16, a connector plug 18, and a body 20 configuredto attach to a patient (e.g., patient's finger, ear, forehead, or toe).Pulse oximetry systems such as system 10 may be utilized to observe theoxygenation or oxygen saturation of a patient's arterial blood toestimate the state of oxygen exchange in the patient's body by emittingwaves into tissue and detecting the waves after dispersion and/orreflection by the tissue. For example, conventional pulse oximetersystems may emit light from two or more light emitting diodes (LEDs)into pulsatile tissue and then detect the transmitted light with a lightdetector (e.g., a photodiode or photo-detector) after the light haspassed through the pulsatile tissue. The amount of transmitted lightthat passes through the tissue varies in accordance with the changingamount of blood constituent in the tissue and the related lightabsorption.

Specifically, as illustrated in FIG. 2, the sensor 14 includes two LEDs30 and a photo-detector 32. The LEDs 30 receive drive signals from themonitor 12 that activate the LEDs 30 and cause them to emit signalsalternatively. The sensor 14 is configured such that light from theactivated LEDs 30 can pass into a patient's tissue 38. After beingtransmitted from (or reflected from) the tissue 38, the dispersed lightis received by the photo-detector 32. The photo-detector 32 converts thereceived light into a photocurrent signal, which is then provided to themonitor 12. The illustrated sensor 14 may also include a memory 34 andan interface 36. The memory 34 and/or the monitor 12 may store softwareapplications in accordance with present embodiments. The interface 36may facilitate communication between the sensor 14 and the monitor 12.

To measure the oxygen saturation of the patient's arterial blood, twodifferent wavelengths of light are typically emitted from the LEDs 30and are used to calculate the ratio of oxygenated hemoglobin oroxyhemoglobin (HbO₂) and deoxygenated hemoglobin or deoxyhemoglobin(Hb), which are dominant hemoglobin components. The light passed throughthe tissue (e.g., tissue 38) is typically selected to include two ormore wavelengths that are absorbed by the blood in an amount related tothe amount of blood constituent present in the blood. Specifically, afirst wavelength for one of the LEDs 30 is typically selected at a pointin the electromagnetic spectrum where the absorption of HbO₂ differsfrom the absorption of reduced Hb. A second wavelength for one of theLEDs 30 is typically selected at a different point in the spectrum wherethe absorption of Hb and HbO₂ differs from those at the firstwavelength. For example, LED wavelength selections for measuring normalblood oxygenation levels typically include a red light emitted atapproximately 660 nm and an infrared light emitted at approximately 900nm.

While various techniques may be utilized to estimate oxygen saturation,in one common technique, the first and second light signals detected bythe light detector from red and infrared light sources are conditionedand processed (e.g., via the monitor 12) to determine AC and DC signalcomponents. For example, FIG. 3 illustrates one method of determining ACand DC components from a plethysmographic waveform, wherein maximum(MAX) and minimum (MIN) measurements of each wavelength are measured andcorrelated as set forth in the following equations:

AC=MAX−MIN

DC=(MAX+MIN)/2.  (Eq. 1)

It should be noted that in other embodiments the maximum (MAX) andminimum (MIN) measurements are not necessarily employed to determine theAC and DC components. Indeed, the AC and DC components may be obtainedby using essentially any pair of points along both the infrared and redlight waveforms.

Once obtained, the AC and DC components may be used to compute amodulation ratio of the red to infrared signals. The modulation ratio isgenerally referred to as “the ratio of ratios” or Ratrat and may berepresented as follows:

$\begin{matrix}{{Ratrat} = {\frac{{AC}_{RED}/{DC}_{RED}}{{AC}_{IR}/{DC}_{IR}}.}} & \left( {{Eq}.\mspace{14mu} 2} \right)\end{matrix}$

The Ratrat at a particular time K or over a designated timestep K may berepresented as follows:

$\begin{matrix}{{{Ratrat} = \frac{v_{K}}{u_{K}}},} & \left( {{Eq}.\mspace{14mu} 3} \right)\end{matrix}$

wherein the variable v_(x) is representative of a value for red lightoptical density over timestep K and the variable u_(K) is representativeof a value for infrared light optical density over timestep K. Atimestep may include multiple optical observations taken over adesignated period of time. For example, a timestep of 1 second mayinclude 53 optical observations, which may be used to determine v_(K)and u_(K).

The Ratrat has been observed to correlate well to arterial blood oxygensaturation, as represented by the following equation:

Ratrat=f(s),  (Eq. 4)

wherein s represents blood oxygen saturation. Pulse oximeters andsensors are typically calibrated empirically by measuring the Ratratover a range of in vivo measured arterial oxygen saturations (SaO₂) on aset of patients (e.g., healthy volunteers). The observed correlation isused in an inverse manner to estimate SpO₂ based on the measured valueof modulation ratios. A correlation to blood concentrations may berepresented by the following equation:

$\begin{matrix}{{\begin{pmatrix}v \\u\end{pmatrix} = {\begin{pmatrix}c_{1} & c_{12} \\c_{21} & c_{22}\end{pmatrix}\begin{pmatrix}{OXY} \\{DEOXY}\end{pmatrix}}},} & \left( {{Eq}.\mspace{14mu} 5} \right)\end{matrix}$

wherein the variables c₁₁, c₁₂, c₂₁, and c₂₂ represent coefficients, theOXY variable represents an actual value of oxygenated bloodconcentration, and the DEOXY variable represents an actual value ofdeoxygenated blood concentration. It should be noted that thecoefficients are a function of wavelength. It should further be notedthat calculating SpO₂ based on the Ratrat is one of various methods thatcan be utilized to calculate the level of SpO₂ in a patient. Indeed, theRatrat is used herein by way of example, and present embodiments are notlimited to the use of the Ratrat in determining SpO₂ levels.

The accuracy of blood flow characteristic estimations determined viapulse oximetry can be impacted by various types of noise andinterference (e.g., electrical noise, physiological noise, and otherinterference). In a pulse oximetry system, noise generally manifests asvariations in detected light values. Thus, in a noisy system, a “dirty”plethsymographic signal may be obtained. For example, if noise isintroduced, the detected light values that define the plethysmographicwaveform in FIG. 3 (a “clean” plethysmographic signal) could beextremely skewed, as illustrated by the plethysmographic waveform inFIG. 4 (a “dirty” plethysmographic signal). Such variations in detectedlight values generally directly impact the calculated value of SpO₂. Forexample, as is clear from the discussion of Ratrat determination above,the value of the Ratrat is dependent on the detected light values, andthe Ratrat directly impacts the calculated values of SpO₂. Thus, whenthe Ratrat is used to calculate an SpO₂ value, errors in determining theRatrat due to noise directly impact the calculation of the SpO₂ value.Similarly, in other calculations of SpO₂, noise can create error in thecalculated value of SpO₂.

Because noise causes errors such as those discussed above, it isdesirable to remove or filter out the effects of noise when determiningan SpO₂ value with pulse oximetry. Traditional methods for estimating apatient's SpO₂ level may limit the impact of noise by utilizinghistorical data. Indeed, traditional methods may store historical valuesof SpO₂ observed in the patient, and the historical values may then beutilized in calculations to limit the effects of noise. For example,historical values may be averaged (e.g., using ensemble averaging) ortrended to facilitate detection and/or filtering of noise or noisy data.Specifically, a current value of the patient's SpO₂ level may beestimated based on averaging numerous historical data points and theestimate may then be compared with a measured value to determine whethernoise is present. If noise is present, the measured value may befiltered or modified based on the historical data.

Rather than storing or utilizing numerous historical data points tofacilitate reducing the effects of noise, in accordance with presentembodiments it may be assumed that the value of the SpO₂ can bepredicted based on an immediately prior value of the SpO₂. This can bediscussed in terms of the Ratrat because the Ratrat correlates with thevalue of SpO₂. Accordingly, the assumption may be summarized by statingthat, in theory, in predicting a future value of the Ratrat (i.e.,Ratrat_(K+1)) based on knowledge of the most recent Ratrat (i.e.,Ratrat_(K)), no additional predictive power can be gained from learningthe historical values of the Ratrat (i.e., Ratrat₀, Ratrat₁, . . . ,Ratrat_(K−1)). For example, the following system may be consideredrepresentative:

Ratrat=t _(K)(Ratrat_(K−1))

v _(K) =u _(K) Ratrat_(K) +n _(K) ^(R),  (Eq. 6)

wherein Ratrat_(K) is a function of saturation at major timestep K,u_(K) is a red optical density at major timestep K, v_(K) is an infraredoptical density at major timestep K, and n_(K) ^(R) representsobservation noise. Observation noise may be defined as noise present inphysical observations (e.g., optical or otherwise), and not error in theprevious or present estimate of saturation. The function t representsthe transition of the Ratrat from one timestep to the next, whichincludes a random variable. The inclusion of a random variable indicatesthat given the Ratrat at a previous time interval (i.e., Ratrat_(K)),the value of the Ratrat at a next time interval (i.e., Ratrat_(K+1)) canbe predicted or estimated, but not necessarily directly calculated.

Embodiments of the present invention assume that SpO₂ (and hence Ratrat)follow a Hidden Markov Model of a given distribution. A Hidden MarkovModel is a statistical model wherein the system being modeled is assumedto be a Markov process with unknown parameters. A Markov Model or MarkovChain may be characterized by the following property: P{X_(k+1)=x|X_(k), X_(k−1), . . . , X₀}=P{X_(k+1)=x|X_(k)}. Only the mostrecent value of the random variable is relevant for predicting the nextvalue. In some embodiments, the model may be further restricted suchthat it only takes values in a certain range, or in a certain finite setof numbers. For example, under further restriction the following isrepresentative: P {X_(k+1)=x|X_(k)}=0 when x is not a number in thatcertain range, or in that certain finite set. It should be noted that insome embodiments, there is no need to restrict the Markov process to afinite range. However, it may be assumed that the range is boundedbetween 0 and 100%, but may take any value in that range. The objectiveof Viterbi's algorithm is to determine the hidden parameters fromobservable parameters. The Hidden Markov Model models the underlyingprocess, which is observed with some level of uncertainty (e.g., viaoptical measurements).

In accordance with present embodiments, first and second portions of adetermination may be combined to establish a probable actual SpO₂ valuebased on a prior SpO₂ value and an initial SpO₂ measurement. In otherwords, present embodiments estimate what the actual SpO₂ value should bebased on a preceding SpO₂ value and the value of an initial SpO₂measurement. Once the probable actual SpO₂ value is determined, thatvalue may be posted as the calculated value. Specifically, in the firstportion of the determination, an immediately prior calculated ormeasured SpO₂ value may be utilized to determine a set of predictivepaths and potential SpO₂ values. These predictive paths and potentialSpO₂ values correspond to what will probably be the next actual SpO₂value. For example, there is generally a high probability that the SpO₂value following the immediately preceding value will be equivalent tothe immediately preceding value. In the second portion of thedetermination, a set of predictive paths and potential SpO₂ values maybe determined based on a current SpO₂ measurement value. For example, aninitial SpO₂ measurement may be utilized to determine probabilitiesassociated with potential SpO₂ values for the actual SpO₂ value. Assuggested above, once probabilities based on the immediately precedingSpO₂ value and the initial SpO₂ measurement are established, they may becombined to predict the actual current SpO₂ value for posting. Thisprocedure is discussed in further detail below.

FIG. 5 is a block diagram representing an example of predictive pathsand potential SpO₂ values for a current reading that may be determinedbased on a previously calculated or measured SpO₂ value. It should benoted that the predictive paths are not necessarily explicitlyconstructed in accordance with present embodiments. In other words, thetransition illustrated in FIG. 5 may be described as a continuum thatdoes not utilize a discrete list of values. Essentially, the predictivepaths and potential SpO₂ values represent a probability distribution(e.g., Markov Model transition probability distribution) of what thenext SpO₂ value is likely to be based solely on the most recentlymeasured and/or determined SpO₂ value. This probability distribution mayrepresent what can be referred to as a probability distribution oftransition.

Calculating these predictive paths and potential SpO₂ values representedin FIG. 5 may be considered the first portion of the procedure fordetermining the SpO₂ value to post as the current value. Accordingly,FIG. 5 may be representative of results from the first portion of theprocedure. Specifically, FIG. 5 includes a previous SpO₂ measurement ordetermination (block 40) having an exemplary value of 93%. Block 40represents the SpO₂ value obtained from the Ratrat corresponding totimestep K, wherein K is the most recent previous timestep. Each of thearrows 42 extending from block 40 represents a probability that thevalue of SpO₂ for the current or next timestep (e.g., K+1 or the valueof SpO₂ for the timestep after that represented by block 40) willcorrespond to one of the values in the corresponding blocks 44, 46, 48,50, and 52. In other words, the arrows 42 represent predictive paths forthe potential SpO₂ values in blocks 44-52. It should be noted that whileblocks 44-52 represent a specific range of values, in other embodimentsthe range and values may be different. Indeed, in some embodiments, therange and values represented by blocks 44-52 may be dependent on aselected model of how saturation can change.

The continuum of probability values is represented by a probabilitydistribution function. As indicated above, blocks 44, 46, 48, 50, and 52are representative of potential SpO₂ values that could be obtained atthe next timestep (i.e., K+1) using the corresponding Ratrat for thattimestep (i.e., Ratrat_(K+1)). These potential values (block 44-52)represent a range of potential SpO₂ values based on establishedrelationships between the value of the timestep between K and K+1 (e.g.,1 second) and the initial or immediately preceding SpO₂ value (e.g.,block 40). The following equation is representative of the relationshipbetween block 40 and blocks 44-52:

p(x)=C Pr{Ratrat _(K)=Ratrat_(K−1) +x|Ratrat _(K−1)},  (Eq. 7)

wherein C represent a constant that can be ignored and the functionPr{a|b} means “the probability that a occurs conditioned on the factthat b was observed to occur.” It may be assumed that a probabilitydistribution function of the transition function t can be estimated by aparticular equation or model. In the case of a continuum, the functionp(x) is a probability density, which is integrated to obtainprobabilities.

The most likely value for SpO₂ that will be obtained in the imminenttimestep (i.e., K+1) based on the known Ratrat (i.e., Ratrat_(K)) isrepresented by block 48, which has the same SpO₂ value as that of theprevious timestep (block 40). Values for the SpO₂ at K+1 that havelesser probabilities of occurrence are dispersed outwardly according toa probability distribution function used to determine the probabilities42 for each potential value (blocks 44-52). For example, in accordancewith some embodiments, a piecewise linear spline, a double exponentialfunction, or Gausian probability density function may be utilized todetermine probabilities (e.g., probabilities 42). Such probabilities 42may indicate that, for example, if the timestep is approximately onesecond, it is highly unlikely that during the transition from K to K+1the SpO₂ level will drop by a certain amount (e.g., twenty points) andthat it is highly likely that the SpO₂ value will remain the same.Accordingly, changes within a certain range will be given a higherprobability value. Generally, these probability values are lower thefurther the corresponding SpO₂ value (blocks 44-52) is from the initialvalue (block 40) (e.g., the probability of the SpO₂ value for timestepK+1 remaining at 93% is greater than that of it changing to 91%) becausechanges in SpO₂ do not typically include large, sudden swings. It shouldbe noted that it may be slightly more likely to have a large increase inthe SpO₂ value in certain situations (e.g., an oxygen starved patientreceiving oxygen).

In accordance with some embodiments, the mean of the probabilitydistribution function for the saturation transition should be theprevious value of SpO₂. In other words, the probability distributionfunction of the saturation transition may be designed such thatsaturation is a Martingale. This generally prevents present embodimentsfrom predicting large fluctuations in saturation levels when presentedwith noiseless optical observations. Moreover, for this reason, it maybe desirable to adjust or tune the probability distribution functionsuch that its variance is minimized, or otherwise controlled. In someembodiments, the probability density function of the saturationtransition is represented by a spline function (i.e., a functioncomposed of portions of polynomials linked together). For example, aspline function utilized in accordance with present embodiments mayinclude a piecewise linear function.

FIG. 6 may be representative of results from the second portion of theprocedure for determining the SpO₂ value to be posted as current. As setforth above, these results may be combined with the results illustratedin FIG. 5 to provide the current SpO₂ reading. Specifically, FIG. 6 is ablock diagram representing predictive paths and the potential SpO₂values for the related timestep based on an initial SpO₂ reading for thecurrent timestep in accordance with embodiments of the presentinvention. Essentially, the predictive paths and potential SpO₂ valuesrepresent a probability distribution of what the next SpO₂ value islikely to be based on an initial measurement. This probabilitydistribution may represent what can be referred to as a probabilitydistribution of measured values or the probability of observing a givenmeasured value.

Specifically, FIG. 6 includes block 54, which represents an SpO₂ value(e.g., 94%±3%) for the current timestep that takes into accountobservational information. Additionally, FIG. 6 includes the blocks 44,46, 48, 50, and 52, which are representative of potential actual SpO₂values that could be obtained for the current time step within a certainlikelihood. Arrows 56, which connect the potential SpO₂ values (blocks44-52) with the value accounting for observational information (block54), are representative of probabilities that given the SpO₂ value isactually a certain value, the (optical) measurements indicate the givenSpO₂ value. For example, the probability along the arrow 56 from block46 to block 54 represents the probability that, given that the SpO₂ isactually 92%, optical observations indicate that the SpO₂ is 94%.However, the procedure is more general that this example. Opticalmeasurements include an estimate of noise (e.g., ±3%). This estimate ofnoise facilitates construction of the arrows 56. When the error is small(e.g., ±0.5%), it is likely that SpO₂ really is the value indicated bythe optical observations (e.g., the probability that the underlying SpO₂is 94% and was observed to be 94% is high, while the probability thatthe underlying SpO₂ value is 92% and was observed to be 94% is low).When the error is larger (e.g., ±6%), the distribution flattens out. Inother words, the probabilities associated with the arrows 56 becomenearly equal when the error is large. The probabilities 56 may berepresented and determined by designated equations, which will bediscussed in further detail below.

As illustrated in FIG. 7, once determined, the probabilities representedby arrows 56 may be combined with the probabilities represented byarrows 42 to determine which potential SpO₂ value should be posted asthe meaningful SpO₂ value. The algorithm represented by FIG. 7 employsprobability density functions. It should be noted that a probabilitydensity function may be defined as the function, f(x), such that given arandom variable X, the following equations applies:

$\begin{matrix}{{P\left( {a \leq x \leq b} \right)} = {\int\limits_{a}^{b}{{f(x)}{{x}.}}}} & \left( {{Eq}.\mspace{14mu} 8} \right)\end{matrix}$

Further, it should be noted that, in present embodiments, theprobability density function may only need to be known up to a constant.

Determining the meaningful SpO₂ value for posting may include thefollowing procedures: let P(x) be a nonzero constant times theprobability density function associated with the Hidden Markov Model(i.e. the transition of SpO₂ values), let Q(x) be a nonzero constanttimes the probability density function associated with the observation,and let W(x)=P(x)Q(x) be some constant times the probability densityfunction of the value of SpO₂ at the current state, conditional on the(optical) observations. This may be represented as follows:

$\begin{matrix}{{{P\left( {{{a \leq X_{k + 1} \leq b}{X_{k}\mspace{14mu} {known}}},{{optical}\mspace{14mu} {observations}}} \right)} = {D{\int\limits_{a}^{b}{{W(x)}{x}}}}},} & \left( {{Eq}.\mspace{14mu} 9} \right)\end{matrix}$

where D is an unknown constant. Viterbi's algorithm, which is a maximumlikelihood estimate algorithm, estimates X_(k+1) by that value x whichmaximizes W(x). Because P(x) and Q(x) are represented in the algorithmas functions (e.g., P(x) is a linear spline, or the probabilitydistribution function of a Gaussian or normal, double exponential, andso forth, and Q(x) is the probability density function of a Gaussian),this maximum can be found using calculus, and not by checking everyvalue between values (e.g., between 91% and 95%).

In the exemplary embodiment illustrated in FIG. 7, block 60 represents abest estimate of saturation at timestep K based on the previous opticalobservations. Probabilities 62 (13) may be based on the Hidden MarkovModel of human physiology. Accordingly, these probabilities 62 may onlydepend on the estimate of saturation, and not on current opticalobservations. The probabilities 64 (Q_(j)) may be based on a model ofoptical noise and optical observations since timestep K. Theprobabilities 64 represent the probability that, if the saturation werej at timestep K+1, the observations would correspond to the observationsmade. Block 66 represents a linear regression of red to infrared opticalsignals over the major time step. Block 68 represents the red toinfrared optical signals over one second.

When the noise in optical observations is assumed to be independentidentically distributed normal random variables with zero mean and agiven variance, the least squares regression slope is a random variablewhose expected value is the underlying Ratrat with a certain variance.In this case, the value of the probabilities 64 may be generated fromthe probability distribution function of the normal. In a specificexample, if the saturation value is assumed to be 90 at timestep K, andoptical observations indicate 91.8±0.5 saturation, the probability thatsaturation is j at timestep K+1 is P_(j)Q_(j), under assumptions ofindependence. Thus, saturation at K+1 may be estimated by that j and aniterative procedure with future optical observations may be invoked.

FIG. 8 is a block diagram of an algorithm for determining the best valueto post for a patient's SpO₂ value based on the value of the Ratrat(i.e., Ratrat_(K−1)) for the previous timestep, the current measurementor measurements for the SpO₂ value, the probabilities 42 and 56 relatingto the actual occurrence of the SpO₂ value, and estimated noise. Thealgorithm is generally referred to by reference number 90. Specifically,algorithm 90 includes several steps for determining an appropriateestimate of the Ratrat for timestep K+1 that is substantially accurateduring periods of noise and/or interference. The determination ofalgorithm 90 is largely based on observations during timestep K+1 and aprovided (e.g., measured) value of the Ratrat for timestep K. It shouldbe noted that it is assumed in accordance with present embodiments thatthe sampling rate of optical observations is higher than the meaningfulposting rate. For example, in one embodiment optical observations aremade at 53 Hz and saturation values are posted at 1 Hz. Intervalsbetween posts may be referred to as timesteps and intervals betweenobservations may be referred to as minor timesteps.

In block 92, an initial estimate of the Ratrat at an initial time or foran initial timestep (i.e., Ratrat₀) is provided. This initial value maysimply include the value of the Ratrat assigned to the timestep prior tothe timestep for which the Ratrat (or the SpO₂ value for posting) isbeing determined. The initial estimate of the Ratrat may be obtainedusing various procedures, such as bootstrapping a value from an initialreading. For example, the first estimate of saturation may be the valueof the current physical measurement. In other embodiments, an overallprobability distribution of the saturation in humans may be determinedand utilized as the transition distribution from an unknown saturationin the previous time step. For example, population studies may provideestimates of saturation in groups of people, which may be utilized withsafety factors (e.g., lower saturation levels for certain groups andgenerally lower saturation level estimates) to provide an initialestimate. This universal distribution approach does not require that theprevious saturation estimate be known. In another embodiment, anestimate of saturation level and an estimate of confidence in thesaturation level may be maintained. For example, information such as “attime step k saturation is in the range of 94.5%-95.499% (posting 94)with 75% certainty” may be maintained. In order to boot strap, theinformation may be initialized as “at time step 1, the saturation is inthe range 89.5%-90.499% (or values provided in accordance with thephysical measurement) with 1% certainty (or a value obtained from thephysical measurement.”

After establishing the initial estimate, in block 94, the opticalobservations over the previous timestep are correlated and a noiseestimate is obtained. Various procedures may be utilized to provide thisnoise estimate. For example, block 94 may include performing a linearregression of all the optical observations over the previous timestep toobtain a best fit slope, which may be referred to by the variable

. Additionally, an estimate of noise may be obtained by determining asample standard deviation of the residuals, which may be referenced bythe variable θ. Under the assumption of normal noise,

is distributed as the true slope plus an unbiased normal random variablewith variance. This variance may be represented as θ²/σ_(ir) ², whereσ_(ir) ² represents the sample standard deviation of the infrared valuesover the previous timestep. The following equation is representative:

$\begin{matrix}{= {\sigma_{k} + {N\left( {o,\frac{\theta^{2}}{\sigma_{x}^{2}}} \right)}}} & \left( {{Eq}.\mspace{14mu} 10} \right)\end{matrix}$

One component of algorithm 90 is the incorporation of a maximumlikelihood estimator. That is, the estimate of Ratrat_(K) is that whichis most likely given the previous estimate and the optical evidence overthe previous major timestep K−1. This maximum likelihood estimation isrepresented by block 96, wherein:

trat_(j)←

trat_(j−1)+ξ  (Eq. 11)

and wherein ξ maximizes the following function:

$\begin{matrix}{{p(\xi)}{e\left( \frac{{- 0.5}\left( {- {Ratrat}_{j - 1} - \xi} \right)^{2}}{\theta^{2}/\sigma_{ir}^{2}} \right)}} & \left( {{Eq}.\mspace{14mu} 12} \right)\end{matrix}$

which is proportional to:

Pr{Ratrat_(j)−Ratrat_(j−1)=ξ|Ratrat_(j−1)=

trat_(j−1) }Pr{

observed|

˜N(Ratrat_(j−1)+ξ,θ²/σ_(ir) ²}  (Eq. 13)

which represents combining (e.g., essentially multiplying) theprobability 42 represented in FIG. 5 with the probability 56 representedin FIG. 6 to determine a posting value based on which of the potentialvalues has the highest probability. Because this is a maximum likelihoodcalculation, it is unnecessary to compute the cumulative distributionfunction of the normal, and p(x) can be known only up to a constant.Thus, the value posted as the meaningful SpO₂ value is represented byEq. 13.

FIGS. 9, 10 and 11 are representations of data acquired by implementingand testing present embodiments against synthetic data using Viterbi'salgorithm. The synthetic data consist of a time signal of saturationwith a constant level of noise added. The results are plotted in severalgraphs in each of FIGS. 9, 10, and 11. The different graphs correspondto different levels of noise in the observation, different choices ofthe variance in the underlying Markov Model, and different choices ofthe transition limits (up and down) of the Markov Model. Each of FIGS.9, 10, and 11 include a first graph (A) with low noise and low variance,a second graph (B) with low noise and high variance, a third graph (C)with medium noise and low variance, a fourth graph (D) with medium noiseand high variance, a fifth graph (E) with high noise and low variance,and a sixth graph (F) with high noise and high variance. Further, eachgraph shows an actual underlying synthetic saturation signal 72,observed saturation with noise added 74, and a maximum likelihoodestimator (i.e., output of the algorithm) 76.

FIGS. 9, 10, and 11 each illustrate slightly different information. Thegraphs in FIG. 9 show a model wherein the maximal decrease in saturationtransition of the Markov Model is one-third the maximal increase insaturation transition. Specifically, the graphs in FIG. 9 illustrate theresults of a maximum likelihood estimation using a Markov Model with asaturation increase allowed at triple the rate of saturation decrease.This approximates physiological reality, as saturation decreases aretypically slow, while increases occur quickly with inspiration. Thegraphs in FIG. 10 show a model wherein the maximal decrease and maximalincrease in saturation transition are equal. Specifically, the graphs inFIG. 10 show the results of a maximum likelihood estimation using aMarkov Model with saturation increase allowed at the same rate ofdecrease. The graphs in FIG. 11 show a model wherein the maximaldecrease is 4.5 times greater than the maximal increase in saturationtransition. Specifically, FIG. 11 shows the results of a maximumlikelihood estimation using a Markov Model with the aforementioned ratiobetween saturation increase and decrease.

While the invention may be susceptible to various modifications andalternative forms, specific embodiments have been shown by way ofexample in the drawings and will be described in detail herein. However,it should be understood that the invention is not intended to be limitedto the particular forms disclosed. Rather, the invention is to cover allmodifications, equivalents and alternatives falling within the spiritand scope of the invention as defined by the following appended claims.

1.-20. (canceled)
 21. A method, comprising: using a monitor for: providing an initial estimate of a value representative of blood oxygen saturation at a current timestep; correlating one or more optical observations obtained during a previous timestep; obtaining a noise estimate based on the correlation; and performing a maximum likelihood calculation to determine a value of blood oxygen saturation that is most likely given the initial estimate, the one or more optical observations, and the noise estimate.
 22. The method of claim 21, wherein correlating the one or more optical observations obtained during the previous timestep comprises performing a linear regression of the one or more optical observations.
 23. The method of claim 21, wherein obtaining the noise estimate comprises determining a sample standard deviation of residuals.
 24. The method of claim 21, wherein performing the maximum likelihood calculation comprises combining a first probability distribution and a second probability distribution.
 25. The method of claim 24, wherein the first probability distribution comprises a probability distribution of potential values of blood oxygen saturation at the current timestep based on the initial estimate.
 26. The method of claim 24, wherein the second probability distribution comprises a probability distribution of observed blood oxygen saturation values based on an actual value.
 27. The method of claim 26, wherein the probability distribution of observed values is based at least in part upon the noise estimate.
 28. The method of claim 24, wherein the first probability distribution comprises a probability distribution of potential values of blood oxygen saturation at the current timestep based on the initial estimate, wherein the second probability distribution comprises a probability distribution of observed blood oxygen saturation values based on an actual value, and wherein performing the maximum likelihood calculation comprises multiplying the first probability distribution by the second probability distribution.
 29. The method of claim 21, wherein obtaining the initial estimate comprises assigning a previous value of blood oxygen saturation at a previous timestep as the initial estimate.
 30. The method of claim 21, wherein obtaining the initial estimate comprises obtaining a current physical measurement of blood oxygen saturation at the current timestep.
 31. A system for providing a meaningful blood oxygen saturation value at a current timestep, comprising: a pulse oximetry monitor configured to: determine a first probability distribution of potential blood oxygen saturation values, based on an initial estimate of a blood oxygen saturation value; determine a second probability distribution of potential blood oxygen saturation values based on an initial measurement of a blood oxygen saturation value; and combine the first and second probability distributions to provide a meaningful blood oxygen saturation value, wherein each probability distribution of potential values includes potential values at a current timestep with associated probabilities of occurrence.
 32. The system of claim 31, comprising a sensor including an emitter configured to emit first and second wavelengths of light, a detector configured to detect the first and second wavelengths of light, and a transmitter configured to transmit photocurrent signals indicative of the first and second wavelengths of light to the pulse oximetry monitor, and wherein the initial measurement is based on the photocurrent signals.
 33. The system of claim 31, wherein the initial estimate comprises a previous value representative of blood oxygen saturation at a preceding timestep.
 34. The system of claim 31, wherein the initial estimate comprises a stored estimate value based on a patient type.
 35. The system of claim 31, wherein the probability distribution of transition is a Markov Model transition probability.
 36. The system of claim 31, wherein the probability distribution of measured values is a linear spline measured value distribution.
 37. A pulse oximeter, comprising: a processing device configured to: provide an initial estimate of a value representative of blood oxygen saturation at a current timestep; provide an initial measurement of a value representative of blood oxygen saturation at a current timestep; determine a noise estimate based on a correlation of one or more optical observations during a previous timestep; and perform a maximum likelihood calculation to determine a value of blood oxygen saturation that is most likely given the initial estimate, the initial measurement, and the noise estimate.
 38. The pulse oximeter of claim 37, wherein the processing device is configured to perform the maximum likelihood calculation by combining a first probability distribution of potential values based on the initial estimate and a second probability distribution of potential values based on the initial measurement and the noise estimate.
 39. The pulse oximeter of claim 37, wherein the processing device is configured to perform a linear regression of the one or more optical observations to correlate the one or more optical observations.
 40. The pulse oximeter of claim 37, wherein the processing device is configured to determine a sample standard deviation of residuals to determine the noise estimate. 